Possible use of Copernicus MACC-II modeling products in EEAs
assessment work
Leonor Tarrasón, Jan Horálek, Laure Malherbe, Philipp Schneider,
Anthony Ung, Bruce Denby, Linton Corbet and Peter de Smet
18tn EIONET workshop on Air Quality Assessment and ManagementDublin, Ireland, 24th and 25th October 2013
Outline
1. Mapping assessments at EEA2. Use of MACC-II ensemble products 3. Results for PM10 and O34. The role of spatial resolution5. Conclusions and recommendations
Outline
1. Mapping assessments at EEA2. Use of MACC-II ensemble products 3. Results for PM10 and O34. The role of spatial resolution5. Conclusions and recommendations
Approaches to AQ assessments
Three different approaches to air quality status assessments:
1. Mapping based on air quality observations2. Mapping based on air quality modelling 3. Mapping based on a combination of
models and measurements
Increasing accuracy in the description of the extent of exceedances in a certain area
Why mapping? Support to national assessments
PM2.5 Source Contribution Grid Cell (10,12)
0
10
20
30
40
50
60
01-N
ov-0
303
-Nov
-03
05-N
ov-0
307
-Nov
-03
09-N
ov-0
311
-Nov
-03
13-N
ov-0
315
-Nov
-03
17-N
ov-0
319
-Nov
-03
21-N
ov-0
323
-Nov
-03
25-N
ov-0
327
-Nov
-03
29-N
ov-0
301
-Dec
-03
03-D
ec-0
305
-Dec
-03
07-D
ec-0
309
-Dec
-03
11-D
ec-0
313
-Dec
-03
15-D
ec-0
317
-Dec
-03
19-D
ec-0
321
-Dec
-03
23-D
ec-0
325
-Dec
-03
27-D
ec-0
329
-Dec
-03
31-D
ec-0
3
Con
cent
ratio
n ( g
/m3 )
PM2.5 - Cell: {10,12}. All sources and BC included
PM2.5: Background EMEP
PM2.5 - Cell: {10,12}. Source: Domestic wood combustion
PM2.5 - Cell: {10,12}. Source: Road traffic
PM2.5 - Cell: {10,12}. All other sources
PM2.5 source contribution in Oslo
Mapping for regulatory purposes
Different mapping approaches in different countries
The recent ETC/ACM report contains an overview of situation on the use of models for regulatory purposes in 2010 and European composite maps
http://acm.eionet.europa.eu/reports/docs/ETCACM_TP_2013_3_CompAQModMaps_v2.pdf
Current Mapping at ETC/ACM• Unified methodology to
estimate background concentrations in Europe in 10x10 km2
• Useful for population exposure analysis
• Includes an analysis of uncertainties and probability of exceedance of limit values
EEAs current mapping methodology• Airbase background rural and
urban stations• EMEP model calculation in
50x50km2
• Meteorological and site parameters
• Residual kriging for rural and urban maps
• Merging Rural and Urban kriging results
PM10, O3 annual averages PM10 36st highest daily average O3 26th highest daily max 8-h avg SOMO35, AOT40
Combined rural and urban concentration map.
PM10 – 36th maximum daily average values, year 2009 Resolution: 10x10 km.
Probability of exceedance of limit value based on standard interpolation error.
Outline
1. Mapping assessments at EEA2. Use of MACC-II ensemble products 3. Results for PM10 and O34. The role of spatial resolution5. Conclusions and recommendations
Possible improvements of background mapping: example for NO2
1. Airbase station data 2. Ordinary kriging of Airbase station data3. Residual kriging of Airbase station data using OMI satellite obs.
OMI NO2 satellite data from NASA Godard GESC DISC 2012 ETC/ACM Task 1.0.2.8
Possible improvements through use of Copernicus MACC-II products
MACC –II ensemble Operational products with 25x25 km2 and 10x10 km2
http://macc-raq.gmes-atmosphere.eu/som_ensemble.php
MACC-II ensemble performance
Background model requirementsModel
requirementsEMEP MACC-II
ensembleSustainability Long term UNECE Long Term Copernicus
Documentation Scientific reviews Scientific reviews
Availability Every year Y-2 Every Year Y-2 ( Y-1 interim)
Model validation Yearly evaluation reports daily evaluation of forecasts
Yearly evaluation reports, daily evaluation of forecasts
Robustness 10 years experience with Unified model
Ensemble approach with EMEP model included
Accuracy CTM state of art Data assimilation approach implies significantly increased accuracy
Adequate spatial resolution
50x50 km2 25x25 km2 10x10 km2 from 2010
Model requirements
EMEP MACC-II ensemble
Sustainability = =Documentation = =Availability +Model validation = =Robustness +Accuracy +Adequate spatial resolution +
for urban background status assessments
Indicator maps evaluated
Rural and urban background maps For years 2009 and 2010
Outline
1. Mapping assessments at EEA2. Use of MACC-II ensemble products 3. Results for PM10 and O34. The role of spatial resolution5. Conclusions and recommendations
Evaluation of EEA mapping results with use of different background models
Identification of meaningful comparisons from available data, avoiding data assimilation issues
20091. Residual kriging using EMEP model background (50x50 km2)2. Residual kriging using EC4MACS hindcast (50x50 km2) 3. Residual kriging using MACC-II ensemble hindcast (25x25km2)4. Residual kriging using EC4MACS hindcast (7x7 km2)
20105. Residual kriging using EMEP model background (50x50 km2)6. Residual kriging using MACC-II ensemble hindcast (10x10km2)
… and allowing for an evaluation of the effect of scale
ETC/ACM residual krigingEvaluation of CTM models used
EMEP MACCenshindcast
Similar mapping results both for O3 and PM10 indicators, with different background model used
Kriging driven by observations
Larger differences between rural and urban kriged maps independently of the models used
EMEP MACC_ens_hindcast
RURA
LUR
BAN
PM10 - rural areas -2009
Significant improvement in performace inherent to residual kriging as the method is developped to optimize RMSE
PM10- urban background - 2009
Significant improvement in performace inherent to residual kriging as the method is developped to optimize RMSE
model vs DA vs kriged
Largest differences in
areas
with few observations,
kriging driven by observations
Is residual kriging better than DA ? Not really, there are caveats in the comparison
Caveats in the present comparison !!!
MACC-II DA does not use the same stations
Not all stations are in MACC-II data DA but all are included in ETC/ACM kriging
Outline
1. Mapping assessments at EEA2. Use of MACC-II ensemble products 3. Results for PM10 and O34. The role of spatial resolution5. Conclusions and recommendations
SOMO35 –rural 2009
50x50 km2 25x25 km2 7x7 km2
SOMO35 –urban background 2009
50x50 km2 25x25 km2 7x7 km2
PM10 – rural areas -2010
MACC-II ensemble in 2010 with 10x10 km2
Improvement with MACC-II ensembleBoth in rural and urban areas
Effect of increased resolution
PM10 general performance
Finer resolution improves the results MACC generally better results for PM10 in 2010 because of incresed resolution
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 4.63 0.30 0.531 y = 0.567x + 8.67 5.81 -0.04 0.728 y = 0.727x + 7.89MACC-ENS hindcast 4.55 0.15 0.553 y = 0.620x + 7.50 5.74 -0.09 0.730 y = 0.724x + 7.75EC4MACS hind. 20x20 4.43 0.13 0.568 y = 0.577x + 8.30 5.93 -0.08 0.703 y = 0.733x + 7.56EC4MACS hind. 7x7 4.21 0.14 0.612 y = 0.645x + 7.00 6.04 -0.06 0.692 y = 0.732x + 7.49
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 7.99 0.51 0.550 y = 0.577x + 14.51 11.38 -0.04 0.715 y = 0.712x + 14.23MACC-ENS hindcast 7.55 0.36 0.599 y = 0.638x + 12.36 11.25 -0.08 0.722 y = 0.716x + 13.67EC4MACS hind. 20x20 7.61 0.54 0.591 y = 0.603x + 13.69 11.29 0.18 0.710 y = 0.731x + 13.26EC4MACS hind. 7x7 7.00 0.55 0.656 y = 0.693x + 10.72 11.69 0.25 0.691 y = 0.714x + 13.98
rural urban background
PM10 annual average
PM10 36th highest value
rural urban background
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 4.63 0.30 0.615 y = 0.642x + 7.28 5.98 -0.19 0.776 y = 0.773x + 6.38MACC-ENS hindcast 4.49 0.29 0.620 y = 0.667x + 6.88 5.78 -0.16 0.789 y = 0.795x + 5.79
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 8.53 0.19 0.646 y = 0.643x + 12.83 11.38 -0.33 0.782 y = 0.773x + 11.14MACC-ENS hindcast 8.64 0.53 0.639 y = 0.663x + 12.44 11.20 -0.21 0.787 y = 0.799x + 10.00
rural urban background
PM10 annual average
PM10 36th highest value
rural urban background
Ozone general performance
MACC general better performance, specially in 2010 with finer resolution
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 8.23 -0.01 0.681 y = 0.709x + 33.43 9.33 0.09 0.642 y = 0.668x + 36.96MACC-ENS hindcast 7.84 0.02 0.710 y = 0.714x + 32.97 9.08 0.12 0.660 y = 0.678x + 35.82EC4MACS hind. 20x20 8.57 0.13 0.656 y = 0.696x + 35.13 9.48 0.06 0.631 y = 0.672x + 36.40EC4MACS hind. 7x7 7.87 0.01 0.708 y = 0.728x + 31.34 9.23 0.06 0.649 y = 0.671x + 36.51
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 1627 9 0.629 y = 0.642x + 1999 1476 -1 0.613 y = 0.619x + 1701MACC-ENS hindcast 1567 -11 0.655 y = 0.661x + 1876 1456 -3 0.624 y = 0.636x + 1623EC4MACS hind. 20x20 1665 16 0.613 y = 0.648x + 1973 1496 -7 0.603 y = 0.620x + 1690EC4MACS hind. 7x7 1626 2 0.629 y = 0.646x + 1969 1460 -5 0.622 y = 0.628x + 1657
rural urban background
Ozone, 26th highest daily 8-hourly maximum
Ozone, SOMO35
rural urban background
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 8.94 0.13 0.682 y = 0.722x + 32.28 9.18 0.04 0.709 y = 0.729x + 30.45MACC-ENS hind. 8.91 0.13 0.684 y = 0.727x + 31.79 9.15 0.04 0.711 y = 0.734x + 29.97
RMSE bias R2 regr.equation RMSE bias R2 regr.equationEMEP 1582 11 0.629 y = 0.652x + 1899 1270 4 0.651 y = 0.667x + 1441MACC-ENS hind. 1566 15 0.637 y = 0.663x + 1843 1272 5 0.650 y = 0.666x + 1445
rural urban background
Ozone, 26th highest daily 8-hourly maximum
Ozone, SOMO35
rural urban background
Conclusions Residual kriging results largely driven by observations Therefore there are larger differences in the urban vs rural maps than
between models used and differences between mapping results are larger where we have
fewer stations!
Similar mapping results independently of the models used although in the analysed cases the MACC-II ensemble shows general better performance
MACC-II ensemble appears to be better when used in 10 x 10 km2, specially for urban background mapping
Recommendations
1. ETC/ACM mapping activities will benefit from the regular use of MACC-II ensemble products both for rural and urban background assessments
2. MACC-II ensemble has long-term sustainability, can be available regularly (yearly assessments) and can provide accurate model results with increased spatial resolution. However, the capabilities of MACC-II ensemble data assimilation results have not been assessed in this context.
3. It is recommended to carry out a dedicated study of the capabilities of the DA data assimilated products from MACC-II ensemble in comparison with EEAs urban and rural background assessment mapping routines.
Thank you for your attention!